Abstract
We propose and test on real data a two-tier estimation strategy for inferring occupancy levels from measurements of CO2 concentration and temperature levels. The first tier is a blind identification step, based either on a frequentist Maximum Likelihood method, implemented using non-linear optimization, or on a Bayesian marginal likelihood method, implemented using a dedicated Expectation-Maximization algorithm. The second tier resolves the ambiguity of the unknown multiplicative factor, and returns the final estimate of the occupancy levels. The overall procedure addresses some practical issues of existing occupancy estimation strategies. More specifically, first it does not require the installation of special hardware, since it uses measurements that are typically available in many buildings. Second, it does not require apriori knowledge on the physical parameters of the building, since it performs system identification steps. Third, it does not require pilot data containing measured real occupancy patterns (i.e., physically counting people for some periods, a typically expensive and time consuming step), since the identification steps are blind.
Original language | English |
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Title of host publication | 14th European Control Conference (ECC 2015), July 15-17, 2015, Linz, Austria |
Place of Publication | New York |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 1315-1320 |
Number of pages | 6 |
ISBN (Electronic) | 978-3-9524-2693-7 |
DOIs | |
Publication status | Published - 16 Nov 2015 |
Externally published | Yes |
Event | 14th European Control Conference, ECC 2015 - Johannes Kepler University, Linz, Austria Duration: 15 Jul 2015 → 17 Jul 2015 Conference number: 14 http://www.ecc15.at/ |
Conference
Conference | 14th European Control Conference, ECC 2015 |
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Abbreviated title | ECC 2015 |
Country/Territory | Austria |
City | Linz |
Period | 15/07/15 → 17/07/15 |
Other | European Control Conference |
Internet address |
Keywords
- Expectation-Maximization
- Maximum Likelihood
- System identification
- management of HVAC systems